Radio frequency (RF) spike noise in magnetic resonance imaging (MRI) is defined as bursts of high amplitude corruptions in the Fourier domain (k-space) that cause Moiré patterns in the reconstructed image. Traditional spike correction techniques rely on the RF receive signal amplitude exceeding a certain present threshold in order to recognize spikes. Detected spikes are then typically replaced by zeros or by local interpolation of k-space neighbors.
Recent developments in compressed sensing have allowed the acquisition of MRI data with a significant relaxation of sampling requirements. In many cases, compressed sensing techniques can allow for reconstruction of full MRI data sets from just 25% or less of the full data set, as would be defined by the Nyquist-Shannon sampling theorem. RF spike noise is typically limited to less than 1% of the k-space data, so reconstructing the correct data in place of the spiked data should be trivial for a method as effective as compressed sensing.